Reassessing Urban Health Interventions: Back to the Future with Google Street View Time Machine

Validity of research linking built environments to health relies on the availability and reliability of data used to measure exposures. As cities transform, it is important to track when and where urban changes occur, to provide detailed information for urban health intervention research. This paper presents an online observation method of the implementation of traffic-calmingfeatures using Google Street View Time Machine. The method is used to validate an existingadministrative database detailing the implementation of curb extensions and speed bumps.

Online observation of curb extensions and speed bumps was conducted for four boroughsin Montreal, Canada, in autumn 2016, and compared with administrative data documenting traffic-calming measures implemented between 2008 and 2014. All images available through the Time Machine function between 2007 and 2016 for 708 intervention sites were visualized online. Records in the administrative database were compared to real-world Google Street View observations and tested in terms of sensitivity, specificity, and positive predicted value.

Google Street View Time Machine allowed the visualization of a median of seven different dates per street intersection and six dates per street segment. This made it possible to analyze built environment changes within 3,973 distinct time periods at 708 locations. Validation of the administrative data regarding presence of an intervention showed 99% (95% CI=97%, 99%) sensitivity, 58% (95% CI=51%, 64%) specificity, and 77% (95% CI=73%, 81%) positive predictive value.

Google Street View Time Machine allowed past (2007-2016) online documentation of microscale urban interventions-curb extensions and speed bumps. The proposed method offers a new way to document historic changes to the built environment, which will be useful for urban health intervention research.